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R-loops have both positive and negative impacts on chromosome functions. To identify toxic R-loops in the human genome, here, we map RNA:DNA hybrids, replication stress markers and DNA double-strand breaks (DSBs) in cells depleted for Topoisomerase I (Top1), an enzyme that relaxes DNA supercoiling and prevents R-loop formation. RNA:DNA hybrids are found at both promoters (TSS) and terminators (TTS) of highly expressed genes. In contrast, the phosphorylation of RPA by ATR is only detected at TTS, which are preferentially replicated in a head-on orientation relative to the direction of transcription. In Top1-depleted cells, DSBs also accumulate at TTS, leading to persistent checkpoint activation, spreading of γ-H2AX on chromatin and global replication fork slowdown. These data indicate that fork pausing at the TTS of highly expressed genes containing R-loops prevents head-on conflicts between replication and transcription and maintains genome integrity in a Top1-dependent manner.

Sequencing microRNA, reduced representation sequencing, Hi-C technology and any method requiring the use of in-house barcodes result in sequencing libraries with low initial sequence diversity. Sequencing such data on the Illumina platform typically produces low quality data due to the limitations of the Illumina cluster calling algorithm. Moreover, even in the case of diverse samples, these limitations are causing substantial inaccuracies in multiplexed sample assignment (sample bleeding). Such inaccuracies are unacceptable in clinical applications, and in some other fields (e.g. detection of rare variants). Here, we discuss how both problems with quality of low-diversity samples and sample bleeding are caused by incorrect detection of clusters on the flowcell during initial sequencing cycles. We propose simple software modifications (Long Template Protocol) that overcome this problem. We present experimental results showing that our Long Template Protocol remarkably increases data quality for low diversity samples, as compared with the standard analysis protocol; it also substantially reduces sample bleeding for all samples. For comprehensiveness, we also discuss and compare experimental results from alternative approaches to sequencing low diversity samples. First, we discuss how the low diversity problem, if caused by barcodes, can be avoided altogether at the barcode design stage. Second and third, we present modified guidelines, which are more stringent than the manufacturer's, for mixing low diversity samples with diverse samples and lowering cluster density, which in our experience consistently produces high quality data from low diversity samples. Fourth and fifth, we present rescue strategies that can be applied when sequencing results in low quality data and when there is no more biological material available. In such cases, we propose that the flowcell be re-hybridized and sequenced again using our Long Template Protocol. Alternatively, we discuss how analysis can be repeated from saved sequencing images using the Long Template Protocol to increase accuracy.

DNA double-strand breaks (DSBs) are among the most lethal types of DNA damage and frequently cause genome instability. Sequencing-based methods for mapping DSBs have been developed but they allow measurement only of relative frequencies of DSBs between loci, which limits our understanding of the physiological relevance of detected DSBs. Here we propose quantitative DSB sequencing (qDSB-Seq), a method providing both DSB frequencies per cell and their precise genomic coordinates. We induce spike-in DSBs by a site-specific endonuclease and use them to quantify detected DSBs (labeled, e.g., using i-BLESS). Utilizing qDSB-Seq, we determine numbers of DSBs induced by a radiomimetic drug and replication stress, and reveal two orders of magnitude differences in DSB frequencies. We also measure absolute frequencies of Top1-dependent DSBs at natural replication fork barriers. qDSB-Seq is compatible with various DSB labeling methods in different organisms and allows accurate comparisons of absolute DSB frequencies across samples. Measuring relative frequencies of DNA double-strand breaks between loci does not provide the full physiological relevance of those breaks. Here Rowicka and colleagues present qDSB-Seq method which uses spike-in double-strand breaks induced by a restriction enzyme to accurately quantify DNA damage.

Eukaryotic DNA replication is elaborately orchestrated to duplicate the genome timely and faithfully. Replication initiates at multiple origins from which replication forks emanate and travel bi-directionally. The complex spatio-temporal regulation of DNA replication remains incompletely understood. To study it, computational models of DNA replication have been developed in S. cerevisiae. However, in spite of the experimental evidence of forks' speed stochasticity, all models assumed that forks' speeds are the same. Here, we present the first model of DNA replication assuming that speeds vary stochastically between forks. Utilizing data from both wild-type and hydroxyurea-treated yeast cells, we show that our model is more accurate than models assuming constant forks' speed and reconstructs dynamics of DNA replication faithfully starting both from population-wide data and data reflecting fork movement in individual cells. Completion of replication in a timely manner is a challenge due to its stochasticity; we propose an empirically derived modification to replication speed based on the distance to the approaching fork, which promotes timely completion of replication. In summary, our work discovers a key role that stochasticity of the forks' speed plays in the dynamics of DNA replication. We show that without including stochasticity of forks' speed it is not possible to accurately reconstruct movement of individual replication forks, measured by DNA combing.

Capture Hi-C analysis reveals that DNA double-strand breaks within transcriptionally active regions of the human genome form clusters that exhibit delayed repair in the G1 phase of the cell cycle. The ability of DNA double-strand breaks (DSBs) to cluster in mammalian cells has been a subject of intense debate in recent years. Here we used a high-throughput chromosome conformation capture assay (capture Hi-C) to investigate clustering of DSBs induced at defined loci in the human genome. The results unambiguously demonstrated that DSBs cluster, but only when they are induced within transcriptionally active genes. Clustering of damaged genes occurs primarily during the G1 cell-cycle phase and coincides with delayed repair. Moreover, DSB clustering depends on the MRN complex as well as the Formin 2 (FMN2) nuclear actin organizer and the linker of nuclear and cytoplasmic skeleton (LINC) complex, thus suggesting that active mechanisms promote clustering. This work reveals that, when damaged, active genes, compared with the rest of the genome, exhibit a distinctive behavior, remaining largely unrepaired and clustered in G1, and being repaired via homologous recombination in postreplicative cells.

DNA double-strand breaks (DSBs) are implicated in various physiological processes, such as class-switch recombination or crossing-over during meiosis, but also present a threat to genome stability. Extensive evidence shows that DSBs are a primary source of chromosome translocations or deletions, making them a major cause of genomic instability, a driving force of many diseases of civilization, such as cancer. Therefore, there is a great need for a precise, sensitive, and universal method for DSB detection, to enable both the study of their mechanisms of formation and repair as well as to explore their therapeutic potential. We provide a detailed protocol for our recently developed ultrasensitive and genome-wide DSB detection method: immobilized direct in situ breaks labeling, enrichment on streptavidin and next-generation sequencing (i-BLESS), which relies on the encapsulation of cells in agarose beads and labeling breaks directly and specifically with biotinylated linkers. i-BLESS labels DSBs with single-nucleotide resolution, allows detection of ultrarare breaks, takes 5 d to complete, and can be applied to samples from any organism, as long as a sufficient amount of starting material can be obtained. We also describe how to combine i-BLESS with our qDSB-Seq approach to enable the measurement of absolute DSB frequencies per cell and their precise genomic coordinates at the same time. Such normalization using qDSB-Seq is especially useful for the evaluation of spontaneous DSB levels and the estimation of DNA damage induced rather uniformly in the genome (e.g., by irradiation or radiomimetic chemotherapeutics). This protocol describes a genome-wide approach for ultrasensitive and quantitative detection of DNA double-strand breaks (DSBs) that relies on encapsulating cells in agarose beads and labeling breaks with biotinylated adapters.

Budding yeast grown under continuous, nutrient-limited conditions exhibit robust, highly periodic cycles in the form of respiratory bursts. Microarray studies reveal that over half of the yeast genome is expressed periodically during these metabolic cycles. Genes encoding proteins having a common function exhibit similar temporal expression patterns, and genes specifying functions associated with energy and metabolism tend to be expressed with exceptionally robust periodicity. Essential cellular and metabolic events occur in synchrony with the metabolic cycle, demonstrating that key processes in a simple eukaryotic cell are compartmentalized in time.

While protein concentrations are physiologically most relevant, measuring them globally is challenging. mRNA levels are easier to measure genome-wide and hence are typically used to infer the corresponding protein abundances. The steady-state condition (assumption that protein levels remain constant) has typically been used to calculate protein concentrations, as it is mathematically convenient, even though it is often not satisfied. Here, we propose a method to estimate genome-wide protein abundances without this assumption. Instead, we assume that the system returns to its baseline at the end of the experiment, which is true for cyclic phenomena (e.g. cell cycle) and many time-course experiments. Our approach only requires availability of gene expression and protein half-life data. As proof-of-concept, we predicted proteome dynamics associated with the budding yeast cell cycle, the results are available for browsing online at http://dynprot.cent.uw.edu.pl/ . The approach was validated experimentally by verifying that the predicted protein concentration changes were consistent with measurements for all proteins tested. Additionally, if proteomic data are available as well, we can also infer changes in protein half-lives in response to posttranslational regulation, as we did for Clb2, a post-translationally regulated protein. The predicted changes in Clb2 abundance are consistent with earlier observations.

Methylation is one of the most common chemical modifications of biologically active molecules and it occurs in all life forms. Its functional role is very diverse and involves many essential cellular processes, such as signal transduction, transcriptional control, biosynthesis, and metabolism. Here, we provide further insight into the enzymatic methylation in S. cerevisiae by conducting a comprehensive structural and functional survey of all the methyltransferases encoded in its genome. Using distant homology detection and fold recognition, we found that the S. cerevisiae methyltransferome comprises 86 MTases (53 well-known and 33 putative with unknown substrate specificity). Structural classification of their catalytic domains shows that these enzymes may adopt nine different folds, the most common being the Rossmann-like. We also analyzed the domain architecture of these proteins and identified several new domain contexts. Interestingly, we found that the majority of MTase genes are periodically expressed during yeast metabolic cycle. This finding, together with calculated isoelectric point, fold assignment and cellular localization, was used to develop a novel approach for predicting substrate specificity. Using this approach, we predicted the general substrates for 24 of 33 putative MTases and confirmed these predictions experimentally in both cases tested. Finally, we show that, in S. cerevisiae, methylation is carried out by 34 RNA MTases, 32 protein MTases, eight small molecule MTases, three lipid MTases, and nine MTases with still unknown substrate specificity.

The eukaryotic cell division cycle depends on an intricate sequence of transcriptional events. Using an algorithm based on maximum-entropy deconvolution, and expression data from a highly synchronized yeast culture, we have timed the peaks of expression of transcriptionally regulated cell cycle genes to an accuracy of 2 min ([almost equal to]1% of the cell cycle time). The set of 1,129 cell cycle-regulated genes was identified by a comprehensive analysis encompassing all available cell cycle yeast data sets. Our results reveal distinct subphases of the cell cycle undetectable by morphological observation, as well as the precise timeline of macromolecular complex assembly during key cell cycle events.